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How to use store_results_dir method in autotest

Best Python code snippet using autotest_python

Trainer.py

Source:Trainer.py

1# ###2# Mathematical expression recognition tool.3# Written as a part of masters thesis at VUT FIT Brno, 20224# Author: Vladislav Halva5# Login: xhalva046# ###7import logging8import os.path9from datetime import datetime10import numpy as np11import torch12from torch import optim13from torch.utils.tensorboard import SummaryWriter14from torch_geometric.loader import DataLoader15from tqdm import tqdm16from src.data.ImageTransformation import ImageTransformation17from src.data.GMathDataset import CrohmeDataset18from src.data.LatexVocab import LatexVocab19from src.definitions.SltEdgeTypes import SltEdgeTypes20from src.definitions.exceptions.ModelParamsError import ModelParamsError21from src.model.Model import Model22from src.utils.loss import calculate_loss23from src.utils.utils import create_attn_gt, split_databatch, compute_single_item_stats, create_latex_result_file24class Trainer:25    """26    Wrapper for training a evaluating the model.27    """28    def __init__(self, config):29        """30        Configures model, tokenizer and summary writer.31        :param config: configuration setup - dictionary generated with Config class32        """33        self.config = config34        # define metaparameters35        self.components_shape = (32, 32)36        self.edge_features = 1037        self.edge_h_size = config['model']['encoder_edge_fsize']38        self.enc_in_size = config['model']['encoder_in_node_fsize']39        self.enc_h_size = config['model']['encoder_hidden_node_fsize']40        self.enc_out_size = config['model']['encoder_out_node_fsize']41        self.dec_in_size = config['model']['decoder_in_fsize']42        self.dec_h_size = config['model']['decoder_hidden_fsize']43        self.emb_size = config['model']['decoder_embed_fsize']44        self.dec_att_size = config['model']['decoder_attn_size']45        self.enc_vgg_dropout_p = config['model']['dropout_encoder_vgg']46        self.enc_gat_dropout_p = config['model']['dropout_encoder_gat']47        self.dec_emb_dropout_p = config['model']['dropout_decoder_init_embed']48        self.dec_att_dropout_p = config['model']['dropout_decoder_attention']49        self.substitute_terms = False50        # use GPU if available51        if config['device'] == 'gpu':52            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")53        else:54            self.device = torch.device("cpu")55        logging.info(f"Device: {self.device}")56        # load or create tokenizer57        if config['vocabulary']['load_tokenizer'] and os.path.exists(config['vocabulary']['tokenizer_filepath']):58            # load59            self.tokenizer = LatexVocab.load_tokenizer(config['vocabulary']['tokenizer_filepath'])60            logging.info(f"Tokenizer loaded from: {config['vocabulary']['tokenizer_filepath']}")61        elif config['vocabulary']['inkml_folder_for_vocab'] is not None and \62                config['vocabulary']['vocab_filepath'] is not None and \63                os.path.exists(config['vocabulary']['vocab_filepath']) and \64                os.path.exists(config['vocabulary']['inkml_folder_for_vocab']):65            # create66            LatexVocab.generate_formulas_file_from_inkmls(67                config['vocabulary']['inkml_folder_for_vocab'],68                config['vocabulary']['vocab_filepath'],69                substitute_terms=self.substitute_terms,70                latex_gt=False, mathml_gt=True)71            self.tokenizer = LatexVocab.create_tokenizer(config['vocabulary']['vocab_filepath'], min_freq=2)72            LatexVocab.save_tokenizer(self.tokenizer, config['vocabulary']['tokenizer_filepath'])73            logging.info(f"Tokenizer created as: {config['vocabulary']['tokenizer_filepath']}")74        else:75            raise ModelParamsError('Vocabulary could not be initialized')76        self.vocab_size = self.tokenizer.get_vocab_size()77        self.end_node_token_id = self.tokenizer.encode("[EOS]", add_special_tokens=False).ids[0]78        logging.info(f"Vocab size: {self.vocab_size}")79        # init model80        now = datetime.now()81        self.model_name = config['model']['model_name'] + '_' + now.strftime("%y-%m-%d_%H-%M-%S")82        self.model = Model(83            self.device, self.edge_features, self.edge_h_size,84            self.enc_in_size, self.enc_h_size, self.enc_out_size, self.dec_in_size, self.dec_h_size, self.emb_size,85            self.dec_att_size,86            self.vocab_size, self.end_node_token_id, self.tokenizer,87            self.enc_vgg_dropout_p, self.enc_gat_dropout_p, self.dec_emb_dropout_p, self.dec_att_dropout_p)88        self.model.double()89        #load mode state dict90        if config['model']['load'] and os.path.exists(config['model']['load_state_dict']):91            self.model.load_state_dict(torch.load(config['model']['load_state_dict'], map_location=self.device))92            logging.info(f"Model loaded: {config['model']['load_state_dict']}")93        self.model.to(self.device)94        # init summary writer95        if config['writer_path'] is not None:96            if os.path.exists(config['writer_path']):97                self.writer = SummaryWriter(os.path.join(config['writer_path'], self.model_name))98            else:99                logging.info("Writer could not be initialized, directory does not exist")100                self.writer = None101        else:102            self.writer = False103        # set temporary data storage104        if config['tmp_data_storage_folder'] is not None and os.path.exists(config['tmp_data_storage_folder']):105            self.temp_path = config['tmp_data_storage_folder']106        else:107            self.temp_path = None108        self.eval_during_training = False109        self.eval_train_settings = None110        self.second_eval_during_training = False111        self.second_eval_train_settings = None112    def unset_eval_during_training(self):113        """114        Disabled first evaluation settings during training.115        """116        self.eval_during_training = False117        self.eval_train_settings = None118    def unset_second_eval_during_training(self):119        """120        Disabled first evaluation settings during training.121        """122        self.second_eval_during_training = False123        self.second_eval_train_settings = None124    def set_eval_during_training(self, images_root, inkmls_root, batch_size, print_stats,125                                 print_item_level_stats, each_nth_epoch=1, beam_search=True, beam_width=3):126        """127        Sets first evaluation of model during training.128        :param images_root: images folder129        :param inkmls_root: inkml files folder130        :param batch_size: batch_size, if beam search needs to be zero131        :param print_stats: print statistics to STDOUT132        :param print_item_level_stats: print item level statistics to STDOUT133        :param each_nth_epoch: run evaluation each n-th epoch134        :param beam_search: if True Beam search will be used for output graph generation, Greedy search otherwise135        :param beam_width: beam width of Beam search136        """137        if os.path.exists(images_root) and os.path.exists(inkmls_root) and each_nth_epoch > 0:138            self.eval_during_training = True139            self.eval_train_settings = {140                'images_root': images_root,141                'inkmls_root': inkmls_root,142                'batch_size': batch_size,143                'print_stats': print_stats,144                'print_item_level_stats': print_item_level_stats,145                'each_nth_epoch': each_nth_epoch,146                'beam_search': beam_search,147                'beam_width': beam_width148            }149    def set_second_eval_during_training(self, images_root, inkmls_root, batch_size, print_stats,150                                 print_item_level_stats, each_nth_epoch=1, beam_search=True, beam_width=3):151        """152        Sets second evaluation of model during training.153        :param images_root: images folder154        :param inkmls_root: inkml files folder155        :param batch_size: batch_size, if beam search needs to be zero156        :param print_stats: print statistics to STDOUT157        :param print_item_level_stats: print item level statistics to STDOUT158        :param each_nth_epoch: run evaluation each n-th epoch159        :param beam_search: if True Beam search will be used for output graph generation, Greedy search otherwise160        :param beam_width: beam width of Beam search161        """162        if os.path.exists(images_root) and os.path.exists(inkmls_root) and each_nth_epoch > 0:163            self.second_eval_during_training = True164            self.second_eval_train_settings = {165                'images_root': images_root,166                'inkmls_root': inkmls_root,167                'batch_size': batch_size,168                'print_stats': print_stats,169                'print_item_level_stats': print_item_level_stats,170                'each_nth_epoch': each_nth_epoch,171                'beam_search': beam_search,172                'beam_width': beam_width173            }174    def train(self, images_root, inkmls_root, epochs, batch_size=1, loss_config=None, save_model_dir=None, save_checkpoint_each_nth_epoch=0):175        """176        Trains model.177        :param images_root: images folder178        :param inkmls_root: inkml files folder179        :param epochs: number of train epochs180        :param batch_size: batch size181        :param loss_config: loss configuration dictionary - coefficients of single loss components182        :param save_model_dir: folder where, model state dicts will be saved183        :param save_checkpoint_each_nth_epoch: how often save state dict checkpoint184        """185        logging.info("\nTraining...")186        optimizer = optim.Adam(self.model.parameters(), lr=0.0003)187        # augment images while training188        transform = ImageTransformation()189        trainset = CrohmeDataset(images_root, inkmls_root, self.tokenizer, self.components_shape, self.temp_path, self.substitute_terms, transform=transform)190        trainloader = DataLoader(trainset, batch_size, True, follow_batch=['x', 'tgt_y', 'gt', 'gt_ml', 'filename'])191        self.model.train()192        for epoch in range(epochs):193            epoch_loss = 0194            epoch_stats = {195                'symbol_count': 0,196                'correct_symbol_count': 0,197                'edge_count': 0,198                'correct_edge_count': 0,199                'src_node_count': 0,200                'correct_src_node_count': 0,201                'attn_relevant_items': 0,202                'attn_block1_abs_diff': 0,203                'attn_block2_abs_diff': 0,204                'attn_block3_abs_diff': 0205            }206            logging.info(f"\nEPOCH: {epoch}")207            for i, data_batch in enumerate(tqdm(trainloader)):208                data_batch = create_attn_gt(data_batch, self.end_node_token_id)209                data_batch = data_batch.to(self.device)210                optimizer.zero_grad()211                out = self.model(data_batch)212                loss = calculate_loss(out, self.end_node_token_id, self.device, self.writer, loss_config=loss_config, writer_idx=epoch * len(trainloader) + i)213                loss.backward()214                # gradient clipping215                torch.nn.utils.clip_grad_value_(self.model.parameters(), clip_value=5.0)216                optimizer.step()217                # training evaluation218                batch_stats = self.evaluate_training(out.detach())219                epoch_stats['symbol_count'] += batch_stats['symbol_count']220                epoch_stats['correct_symbol_count'] += batch_stats['correct_symbol_count']221                epoch_stats['edge_count'] += batch_stats['edge_count']222                epoch_stats['correct_edge_count'] += batch_stats['correct_edge_count']223                epoch_stats['src_node_count'] += batch_stats['src_node_count']224                epoch_stats['correct_src_node_count'] += batch_stats['correct_src_node_count']225                epoch_stats['attn_relevant_items'] += batch_stats['attn_relevant_items']226                epoch_stats['attn_block1_abs_diff'] += batch_stats['attn_block1_abs_diff']227                epoch_stats['attn_block2_abs_diff'] += batch_stats['attn_block2_abs_diff']228                epoch_stats['attn_block3_abs_diff'] += batch_stats['attn_block3_abs_diff']229                if self.writer:230                    self.writer.add_scalar('ItemAttnMeanAbsDiff_blk1/train', batch_stats['attn_block1_mean_abs_diff'], epoch * len(trainloader) + i)231                    self.writer.add_scalar('ItemAttnMeanAbsDiff_blk2/train', batch_stats['attn_block2_mean_abs_diff'], epoch * len(trainloader) + i)232                    self.writer.add_scalar('ItemAttnMeanAbsDiff_blk3/train', batch_stats['attn_block3_mean_abs_diff'], epoch * len(trainloader) + i)233                epoch_loss += loss.item()234                if self.writer:235                    self.writer.add_scalar('ItemLoss/train', loss.item(), epoch * len(trainloader) + i)236            logging.info(f" epoch loss total: {epoch_loss}")237            logging.info(f" epoch loss avg:   {epoch_loss/len(trainloader)/batch_size}")238            if self.writer:239                self.writer.add_scalar('EpochLossTotal/train', epoch_loss, epoch)240                self.writer.add_scalar('EpochLossAvg/train', epoch_loss/len(trainloader)/batch_size, epoch)241            if save_checkpoint_each_nth_epoch != 0 and epoch % save_checkpoint_each_nth_epoch == save_checkpoint_each_nth_epoch - 1:242                # save model checkpoint243                save_model_check_name = self.model_name + '_' + str(epoch) + '.pth'244                torch.save(self.model.state_dict(), os.path.join(save_model_dir, save_model_check_name))245            epoch_stats['symbol_acc'] = epoch_stats['correct_symbol_count'] / epoch_stats['symbol_count'] if epoch_stats['symbol_count'] > 0 else 0246            epoch_stats['edge_acc'] = epoch_stats['correct_edge_count'] / epoch_stats['edge_count'] if epoch_stats['edge_count'] > 0 else 0247            epoch_stats['src_symbol_acc'] = epoch_stats['correct_src_node_count'] / epoch_stats['src_node_count'] if epoch_stats['src_node_count'] > 0 else 0248            epoch_stats['attn_block1_mean_abs_diff'] = epoch_stats['attn_block1_abs_diff'] / epoch_stats['attn_relevant_items'] if epoch_stats['attn_relevant_items'] > 0 else 0249            epoch_stats['attn_block2_mean_abs_diff'] = epoch_stats['attn_block2_abs_diff'] / epoch_stats['attn_relevant_items'] if epoch_stats['attn_relevant_items'] > 0 else 0250            epoch_stats['attn_block3_mean_abs_diff'] = epoch_stats['attn_block3_abs_diff'] / epoch_stats['attn_relevant_items'] if epoch_stats['attn_relevant_items'] > 0 else 0251            if self.writer:252                self.writer.add_scalar('SetSymAcc/train', epoch_stats['symbol_acc'], epoch)253                self.writer.add_scalar('SetEdgeAcc/train', epoch_stats['edge_acc'], epoch)254                self.writer.add_scalar('SetSrcSymAcc/train', epoch_stats['src_symbol_acc'], epoch)255                self.writer.add_scalar('SetAttnMeanAbsDiff_blk1/train', epoch_stats['attn_block1_mean_abs_diff'], epoch)256                self.writer.add_scalar('SetAttnMeanAbsDiff_blk2/train', epoch_stats['attn_block2_mean_abs_diff'], epoch)257                self.writer.add_scalar('SetAttnMeanAbsDiff_blk3/train', epoch_stats['attn_block3_mean_abs_diff'], epoch)258            logging.info(f" symbol class acc: {epoch_stats['symbol_acc'] * 100:.3f}%")259            logging.info(f" edge class acc:   {epoch_stats['edge_acc'] * 100:.3f}%")260            if self.eval_during_training and epoch % self.eval_train_settings['each_nth_epoch'] == self.eval_train_settings['each_nth_epoch'] - 1:261                # model first evaluation262                self.evaluate(263                    self.eval_train_settings['images_root'],264                    self.eval_train_settings['inkmls_root'],265                    self.eval_train_settings['batch_size'],266                    self.writer, epoch,267                    self.eval_train_settings['print_stats'],268                    self.eval_train_settings['print_item_level_stats'],269                    beam_search=self.eval_train_settings['beam_search'],270                    beam_width=self.eval_train_settings['beam_width']271                )272            if self.second_eval_during_training and epoch % self.second_eval_train_settings['each_nth_epoch'] == self.second_eval_train_settings['each_nth_epoch'] - 1:273                # model second evaluation274                self.evaluate(275                    self.second_eval_train_settings['images_root'],276                    self.second_eval_train_settings['inkmls_root'],277                    self.second_eval_train_settings['batch_size'],278                    self.writer, epoch,279                    self.second_eval_train_settings['print_stats'],280                    self.second_eval_train_settings['print_item_level_stats'],281                    beam_search=self.second_eval_train_settings['beam_search'],282                    beam_width=self.second_eval_train_settings['beam_width'],283                    eval_id=2284                )285        if save_model_dir is not None and os.path.exists(save_model_dir):286            # save model final state dict287            save_model_final_name = self.model_name + '_final.pth'288            torch.save(self.model.state_dict(), os.path.join(save_model_dir, save_model_final_name))289            logging.info(f"Model saved as: {os.path.join(save_model_dir, save_model_final_name)}")290    def evaluate_training(self, data):291        """292        Model training-time batch evaluation.293        :param data: model output294        :return: stats dictionary295        """296        stats = {}297        # evaluate nodes predictions = symbols298        y_pred = torch.argmax(data.y_score, dim=1)299        target_symbols = data.tgt_y300        predicted_symbols = y_pred301        stats['symbol_count'] = target_symbols.shape[0]302        stats['correct_symbol_count'] = torch.sum((target_symbols == predicted_symbols))303        # evaluate edges predictions304        tgt_edge_pc_indices = ((data.tgt_edge_type == SltEdgeTypes.PARENT_CHILD).nonzero(as_tuple=True)[0])305        tgt_pc_edge_relation = data.tgt_edge_relation[tgt_edge_pc_indices]306        out_pc_edge_relation = data.y_edge_rel_score[tgt_edge_pc_indices]307        out_pc_edge_relation = out_pc_edge_relation.argmax(dim=-1)308        stats['edge_count'] = tgt_pc_edge_relation.shape[0]309        stats['correct_edge_count'] = torch.sum((tgt_pc_edge_relation == out_pc_edge_relation))310        # evaluate src symbol prediction311        x_pred = torch.argmax(data.x_score, dim=1)312        x_gt_node = data.attn_gt.argmax(dim=0)313        x_gt = data.tgt_y[x_gt_node]314        stats['src_node_count'] = x_gt.shape[0]315        stats['correct_src_node_count'] = torch.sum(x_gt == x_pred)316        # evaluate attention accuracy317        alpha_batch_mask = (data.y_batch.unsqueeze(1) - data.x_batch.unsqueeze(0) != 0).long()318        no_end_node_indices = (data.tgt_y != self.end_node_token_id)319        no_end_node_mask = no_end_node_indices.unsqueeze(1).repeat(1, data.x.shape[0])320        relevant_attn_mask = torch.logical_and(alpha_batch_mask, no_end_node_mask)321        relevant_items_count = torch.sum(relevant_attn_mask.long())322        attn_gt = data.attn_gt323        block1_attn = data.gcn1_alpha * relevant_attn_mask324        block2_attn = data.gcn2_alpha * relevant_attn_mask325        block3_attn = data.gcn3_alpha * relevant_attn_mask326        block1_abs_diff = torch.abs(attn_gt - block1_attn).sum()327        block2_abs_diff = torch.abs(attn_gt - block2_attn).sum()328        block3_abs_diff = torch.abs(attn_gt - block3_attn).sum()329        block1_mean_abs_diff = block1_abs_diff / relevant_items_count330        block2_mean_abs_diff = block2_abs_diff / relevant_items_count331        block3_mean_abs_diff = block3_abs_diff / relevant_items_count332        stats['attn_relevant_items'] = relevant_items_count333        stats['attn_block1_abs_diff'] = block1_abs_diff334        stats['attn_block2_abs_diff'] = block2_abs_diff335        stats['attn_block3_abs_diff'] = block3_abs_diff336        stats['attn_block1_mean_abs_diff'] = block1_mean_abs_diff337        stats['attn_block2_mean_abs_diff'] = block2_mean_abs_diff338        stats['attn_block3_mean_abs_diff'] = block3_mean_abs_diff339        return stats340    def evaluate(self, images_root, inkmls_root, batch_size=1, writer=False, epoch=None, print_stats=True,341                 print_item_level_stats=False, store_results_dir=None, results_author='', beam_search=True, beam_width=3, eval_id=""):342        """343        Evaluation of model.344        :param images_root: images folder345        :param inkmls_root: inkml files folder346        :param batch_size: batch size (has to be 1 if beam search)347        :param writer: summary writer object348        :param epoch: training epoch if during training349        :param print_stats: print statistics to STDOUT350        :param print_item_level_stats: print item level statistict to STDOUT351        :param store_results_dir: folder where recognition results shall be stored (LaTeX strings)352        :param results_author: results author signature353        :param beam_search: if True Beam search used to generate output graph, Greedy search otherwise354        :param beam_width: beam width of beam search355        :param eval_id: distinction of evaluation configuration for summary writer356        :return:357        """358        logging.info("\nEvaluation...")359        if beam_search:360            logging.info(f"Beam search with beam width: {beam_width}")361        else:362            logging.info(f"Greedy search")363        self.model.eval()364        if store_results_dir is None or not os.path.exists(store_results_dir):365            store_results_dir = None366        # load data367        testset = CrohmeDataset(images_root, inkmls_root, self.tokenizer, self.components_shape, self.temp_path, self.substitute_terms)368        testloader = DataLoader(testset, batch_size, False, follow_batch=['x', 'tgt_y', 'gt', 'gt_ml', 'filename'])369        # init statistics370        stats = {371            'exact_match': 0,372            'exact_match_1': 0,373            'exact_match_2': 0,374            'exact_match_3': 0,375            'exact_match_pct': 0,376            'exact_match_1_pct': 0,377            'exact_match_2_pct': 0,378            'exact_match_3_pct': 0,379            'structure_match': 0,380            'structure_match_pct': 0,381            'edit_distances_seq': [],382            'edit_distance_seq_avg': 0383        }384        with torch.no_grad():385            for i, data_batch in enumerate(tqdm(testloader)):386                data_batch = create_attn_gt(data_batch, self.end_node_token_id)387                data_batch = data_batch.to(self.device)388                out = self.model(data_batch, beam_search, beam_width)389                if self.device == torch.device('cuda'):390                    out = out.cpu()391                # split result batch to separate data elements392                out_elems = split_databatch(out)393                for out_elem in out_elems:394                    # evaluate each batch item separately395                    item_stats = compute_single_item_stats(out_elem, self.tokenizer)396                    stats['edit_distances_seq'].append(item_stats['edit_distance_seq'])397                    stats['structure_match'] += 1 if item_stats['slt_diff']['structure_match'] else 0398                    stats['exact_match'] += 1 if item_stats['slt_diff']['exact_match'] else 0399                    stats['exact_match_1'] += 1 if item_stats['slt_diff']['exact_match_1'] else 0400                    stats['exact_match_2'] += 1 if item_stats['slt_diff']['exact_match_2'] else 0401                    stats['exact_match_3'] += 1 if item_stats['slt_diff']['exact_match_3'] else 0402                    if print_item_level_stats:403                        seq_symbol_accuracy = 0404                        if item_stats['seq_symbols_count'] > 0:405                            seq_symbol_accuracy = item_stats['seq_correct_symbols_count'] / item_stats['seq_symbols_count']406                        logging.info(f"\n  gt symbols:        {item_stats['gt_node_symbols']}")407                        logging.info(f"  pr symbols:        {item_stats['pred_node_symbols']}")408                        logging.info(f"  seq-sym-acc:       {seq_symbol_accuracy * 100:.5f}%")409                        logging.info(f"  gt latex:          {item_stats['latex_gt']}")410                        logging.info(f"  pr latex:          {item_stats['latex_pred']}")411                        logging.info(f"  e-distance:        {item_stats['edit_distance_seq']}")412                        logging.info(f"  SLT struct-match:  {item_stats['slt_diff']['structure_match']}")413                        logging.info(f"  SLT exact-match:   {item_stats['slt_diff']['exact_match']}")414                        logging.info(f"  SLT exact-match-1: {item_stats['slt_diff']['exact_match_1']}")415                        logging.info(f"  SLT exact-match-2: {item_stats['slt_diff']['exact_match_2']}")416                        logging.info(f"  SLT exact-match-3: {item_stats['slt_diff']['exact_match_3']}")417                        logging.info(f"  SLT sym-cls-err:   {item_stats['slt_diff']['node_class_errors']}")418                        logging.info(f"  SLT edge-cls-err:  {item_stats['slt_diff']['edge_class_errors']}")419                    if store_results_dir is not None:420                        create_latex_result_file(store_results_dir, out_elem.filename, item_stats['latex_pred'], results_author)421                    if writer and epoch is not None:422                        self.writer.add_scalar('ItemEditDistSeq/eval', item_stats['edit_distance_seq'], epoch + len(testloader) + i)423        stats['exact_match_pct'] = stats['exact_match'] / len(testset) if len(testset) > 0 else 0424        stats['exact_match_1_pct'] = stats['exact_match_1'] / len(testset) if len(testset) > 0 else 0425        stats['exact_match_2_pct'] = stats['exact_match_2'] / len(testset) if len(testset) > 0 else 0426        stats['exact_match_3_pct'] = stats['exact_match_3'] / len(testset) if len(testset) > 0 else 0427        stats['structure_match_pct'] = stats['structure_match'] / len(testset) if len(testset) > 0 else 0428        stats['edit_distances_seq_avg'] = np.asarray(stats['edit_distances_seq']).mean()429        if print_stats:430            logging.info(f" exact-match:    {stats['exact_match_pct']*100:.3f}% = {stats['exact_match']}")431            logging.info(f" exact-match -1: {stats['exact_match_1_pct']*100:.3f}% = {stats['exact_match_1']}")432            logging.info(f" exact-match -2: {stats['exact_match_2_pct']*100:.3f}% = {stats['exact_match_2']}")433            logging.info(f" exact-match -3: {stats['exact_match_3_pct']*100:.3f}% = {stats['exact_match_3']}")434            logging.info(f" struct-match:   {stats['structure_match_pct']*100:.3f}% = {stats['structure_match']}")435            logging.info(f" e-dist avg: {stats['edit_distances_seq_avg']:.3f}")436        if writer and epoch is not None:437            self.writer.add_scalar('SetExactMatch/eval' + str(eval_id), stats['exact_match_pct'], epoch)438            self.writer.add_scalar('SetExactMatch-1/eval' + str(eval_id), stats['exact_match_1_pct'], epoch)439            self.writer.add_scalar('SetExactMatch-2/eval' + str(eval_id), stats['exact_match_2_pct'], epoch)440            self.writer.add_scalar('SetExactMatch-3/eval' + str(eval_id), stats['exact_match_3_pct'], epoch)441            self.writer.add_scalar('SetStructMatch/eval' + str(eval_id), stats['structure_match_pct'], epoch)442            self.writer.add_scalar('SetEditDistSeqAvg/eval' + str(eval_id), stats['edit_distances_seq_avg'], epoch)443        self.model.train()...

Louvain.py

Source:Louvain.py

1# -*- coding: utf-8 -*-2"""3Created on Tue Aug 25 15:37:44 20204Louvain community detection method5Scoring method to see how well the correlations plus Louvain re-create the original populations6@author: owen7"""8import pointwise_correlation as pc9import community as community_louvain10import matplotlib.pyplot as plt11import matplotlib.cm as cm12import networkx as nx13import pandas as pd14import numpy as np15import collections16import scipy.stats as stats17pd.set_option("display.precision", 3)18pd.set_option("display.max_rows", 25)19pd.set_option("display.expand_frame_repr", False)20class dodgy_indices_analysis():21    22    def __init__(self,dodgy_indices=[]):23        self.verbose = True24        self.index_df=pd.read_csv("{0}".format(pc.INDEX_FILE),index_col=0)25        if not len(dodgy_indices):26            self.dodgy_indices = [527, 545, 707, 1136, 1308, 1649, 1701, 1803, 1884, 1994, 2493, 2535]27        self.columns = ['index','pop_id','expected_pop_size','actual_pop_size',28                        'expected_comparisons','actual_comparisons',29                        'event_beta', 'length of time series']30        self.results = pd.DataFrame(columns = self.columns)31        self.next_index = 032        self.iterate_over_dodgy_indices()33        34        35    def analyse_inconsistent_metaparams(self,v1_df,v2_df,params,meta_params):36        # measure number of comparisons within each population from raw data37        # determine which population is missing comparisons38        # look for unusual parameter values for this population39        40        v1_matching = v1_df.loc[v1_df['name1'] == v1_df['name2']]41        42        #print(v1_matching.head(10))43        # iterate through each population in the metaparams index44        for pop in meta_params.index[:-2]:45            n = meta_params.loc[pop,'n']46            expected_comparisons = int(n*(n-1)/2)47            # select only this population from the matching dfs48            # make sure type of population name is going to work in dataframe49            try:50                pop_name = int(pop)51            except ValueError:52                pop_name = pop53            54            pop_comparisons = len(v1_matching.loc[v1_matching['name1'] == pop_name].index)55            if pop_comparisons:56                actual_pop_size = 257                while actual_pop_size*(actual_pop_size-1)/2 < pop_comparisons:58                    actual_pop_size+=159            else:60                actual_pop_size = 161            62            beta = params.loc[pop,'prior process beta']63            length = meta_params.loc[pop,'T']64            65            self.results.loc[self.next_index,self.columns] = [self.current_index,pop,n,actual_pop_size,66                                                                 expected_comparisons,pop_comparisons,67                                                                 beta,length]      68            self.next_index += 169            if not pop_comparisons == expected_comparisons:70                if self.verbose:71                    print("Expected {0} comparisons from population size {1}".format(expected_comparisons,n))72                    print("Found {0} comparisons for population id {1}".format(pop_comparisons,pop))73                    print(meta_params.loc[pop, 'T'])74                    print(params.loc[pop, 'prior process beta'])75                    print(params.loc[pop+"_noise" , 'betas'])76            77    78    def check_consistency_of_population_sizes(self,raw_data_dir):79        # load in all relevant data in the directory80        meta_params = pd.read_csv("{0}/meta_params.csv".format(raw_data_dir), index_col = 0)81        params = pd.read_csv("{0}/population_parameters.csv".format(raw_data_dir), index_col = 0)82        v1_df = pd.read_csv("{0}/{1}".format(raw_data_dir,'sigma_v1_correlations.csv'),index_col = 0)83        v2_df = pd.read_csv("{0}/{1}".format(raw_data_dir,'sigma_v2_correlations.csv'),index_col = 0)84        85        #print(pd.DataFrame(meta_params))86        pop_sizes = meta_params['n'].dropna()87        #print(meta_params['T'])88        #print(pop_sizes)89        k=sum(pop_sizes)90        #k = sum([pop_sizes.loc[i] for i in pop_sizes.index])91        expected_comparisons = int((k)*(k-1)/2)92        assert len(v1_df.index) == len(v2_df.index)93        self.analyse_inconsistent_metaparams(v1_df,v2_df,params,meta_params)94        95        if not len(v1_df.index) == expected_comparisons:96            return False97        98        return True      99            100    def iterate_over_dodgy_indices(self):101        for self.current_index in self.dodgy_indices:102            data_dir = self.index_df.loc[self.current_index,'raw data directory']103            if self.verbose and not self.check_consistency_of_population_sizes(data_dir):104                print("Inconsistency in index {0} - details above".format(self.current_index))105class Louvain_methods():106    107    def __init__(self,df_results, version = 'Unknown', p_values_graph_setup_option = 'weights', resolution = 1, recursion_level = 0,108                 show_dfs = False, store_results_dir= "", Louvain_version = '1', verbose=False):109        """110        Parameters111        ----------112        df_results : TYPE DataFrame113            DESCRIPTION. df_results must have the following columns:114            'object1' giving the id of the first object115            'object2' giving the id of the second object116            'name1' giving the name of the population of object1117            'name2' giving the name of the population of object2118            'p_value' giving the probabilty of correlation between the two objects119        p_values_graph_setup_option : TYPE string120            DESCRIPTION options are 'random' to initalise complete graph with random edge weights;121                                    'weights' to initialise complete graph with correlation p-values as edge weights122                                    'edges' to selecte each edge (unweighted) with probability given by correlation p-value123        resolution : TYPE float, optional124            DESCRIPTION. The default is 1. Passed directly to community_Louvain methods125        show_dfs : TYPE boolean, optional126            DESCRIPTION. The default is False.127        store_results_dir : TYPE string, optional128            DESCRIPTION. The default is "". If passed, f-scores and confusion matrices are stored129        version : TYPE string, optional130            DESCRIPTION. The default is None. Options are 'v1' for sigma version 1 or 'v2' for sigma version 2131        Louvain_version : TYPE string, optional132            DESCRIPTION. The default is '1', referring to standard Louvain method maximising modularity133                         If this is set to '2', the level of the dendrogram giving the nearest number of clusters to the number of populations is selected134                         If this is set to '3', the standard Louvain method is run; then on each partition found, Louvain is run again135                         If this is set to '4', Louvain is run recursively on each partition found until there is no change in the total number of partitions136                         137        verbose : TYPE, optional138            DESCRIPTION. The default is False.139        Returns140        -------141        None.142        """143    144        self.graph_setup = p_values_graph_setup_option145        self.verbose = verbose146        self.Louvain_version = Louvain_version147        self.sigma_version = version148        self.show_dfs = show_dfs149        self.store_results_dir = store_results_dir150        self.resolution = resolution151        self.recursion_level = recursion_level152        153        # randomise order of results154        self.df_input_correlations = df_results.sample(frac=1)155        # create lists of the population names and the object ids (nodes)156        self.names=list(set(self.df_input_correlations['name1']).union(set(self.df_input_correlations['name2'])))157        self.nodes=list(set(self.df_input_correlations['object1']).union(set(self.df_input_correlations['object2'])))158        if verbose:159            print("There are {0} populations and a total of {1} individuals".format(len(self.names), len(self.nodes)))160         161        # create dictionary to look up the population name for any given object id (node)162        #name_of_node={self.df_input_correlations.loc[i,'object1']:self.df_input_correlations.loc[i,'name1'] for i in self.df_input_correlations.index}163        self.name_of_node = {}164        pop_sizes = collections.defaultdict(int)165        for i in self.df_input_correlations.index:166            id1 = self.df_input_correlations.loc[i,'object1']167            name1 = self.df_input_correlations.loc[i,'name1']168            id2 = self.df_input_correlations.loc[i,'object2']169            name2 = self.df_input_correlations.loc[i,'name2']   170            171            # test consistency of populations and names172            if id1 in self.name_of_node.keys():173                assert(self.name_of_node[id1] == name1)  174            else:175                pop_sizes[name1] += 1176                self.name_of_node[id1] = name1177                178            if id2 in self.name_of_node.keys():179                assert(self.name_of_node[id2] == name2)  180            else:181                pop_sizes[name2] += 1182                self.name_of_node[id2] = name2183           184        185        if self.verbose:186            print("Population sizes based on names in dataframe are {0}".format(pd.DataFrame.from_dict(pop_sizes, orient = 'index').sort_index()))187        188        self.initialise_graph()189        self.make_partition()190                  191            192    def initialise_graph(self):193        #set up graph with time series as nodes and edge weights given by correlations194        self.graph=nx.Graph()195        self.graph.add_nodes_from(self.nodes)196        if self.graph_setup == 'random':197            if self.verbose:198                print("Testing random graph")199            self.graph.add_edges_from([(self.df_input_correlations.loc[i]['object1'],self.df_input_correlations.loc[i]['object2'],200                               {'weight': np.random.random()}) 201                          for i in self.df_input_correlations.index])202       203        elif self.graph_setup == 'weights':204            self.graph.add_edges_from([(self.df_input_correlations.loc[i]['object1'],self.df_input_correlations.loc[i]['object2'],205                               {'weight': self.df_input_correlations.loc[i]['p-value']}) 206                          for i in self.df_input_correlations.index])207            if self.verbose:208                print("Passing p-values as weights for each edge")209        elif self.graph_setup == 'edges':210            self.graph.add_edges_from([(self.df_input_correlations.loc[i]['object1'],self.df_input_correlations.loc[i]['object2'])211                             for i in self.df_input_correlations.index if np.random.random()<self.df_input_correlations.loc[i]['p-value']])212            if self.verbose:213                print("Assigning each edge with probability given by corresponding correlation p-value")214    def make_partition(self):215        if self.Louvain_version == '1':216            # maximise modularity using greedy algorithm217            self.partition = community_louvain.best_partition(self.graph,weight='weight',resolution = self.resolution)218        elif self.Louvain_version == '2':219            # choose the partition with the best number of clusters220            dendro = community_louvain.generate_dendrogram(self.graph)221            # default to the lowest level (largest number of clusters)222            level = 0223            self.partition = community_louvain.partition_at_level(dendro,level)224            225            # if a better partition (closer to the correct number of populations) can be found then use it instead226            for level in range(len(dendro)-1):227                current_partition = community_louvain.partition_at_level(dendro,level)228                #print("Partition at level {0} is {1}".format(level,current_partition))229                clusters = len(set(current_partition.values()))230                if clusters<len(self.names):231                    break232                    233            if level:234                last_partition = community_louvain.partition_at_level(dendro,level-1)235                last_clusters = len(set(last_partition.values()))236                if abs(len(self.names)-clusters)<abs(last_clusters - len(self.names)):237                    self.partition = current_partition238                else:239                    self.partition = last_partition240        241        elif self.Louvain_version == '3':242            # find best partition243            self.partition = community_louvain.best_partition(self.graph,weight='weight',resolution = self.resolution)244            node_groupings = self.split_partition_into_nodes()245            recursively_analyse_subgraphs(self, to_depth = 1)246        247        elif self.Louvain_version == '4':248            # find best partition249            self.partition = community_louvain.best_partition(self.graph,weight='weight',resolution = self.resolution)250            self.node_groupings = self.split_partition_into_nodes()251            self.recursively_analyse_subgraphs(self)252                253    def split_partition_into_nodes(self):254        if self.verbose:255            print("The set of partitions is {0}".format(set(self.partition.values())))256        # create a list of each set of nodes that corresponds to a separate partitions257        node_groupings = [[node for node in self.partition.keys() if self.partition[node] == subgraph_number]258                                 for subgraph_number in set(self.partition.values())]259        if self.verbose:260            print("The partition is {0}".format(self.format_partition(self.partition)))261            print("The node-groupings are therefore {0}".format(self.format_partition(node_groupings)))262        assert(len(self.nodes) == sum([len(nodes) for nodes in node_groupings]))263        264        return node_groupings265    266    def recursively_analyse_subgraphs(self, to_depth = None):267        if self.verbose:268            print("Starting recursion level {0}".format(self.recursion_level))269        # if the graph has only one partition, or the desired recursion level has been reached,break out of the recursion270        if to_depth == self.recursion_level or len(self.node_groupings) == 1:271            if self.verbose:272                print("Returning from recursion level {0}".format(self.recursion_level))273            return None    274        275        # iterate through each group of nodes and instantiate a new Louvain_methods object for each276        new_partition = {}277        partition_number = 0278        for i,nodes in enumerate(self.node_groupings):279            df = self.df_input_correlations.loc[(self.df_input_correlations['object1'].isin(nodes))280                                                    & (self.df_input_correlations['object2'].isin(nodes))]281                282            sub_Louvain = Louvain_methods(df,p_values_graph_setup_option = self.graph_setup,resolution = self.resolution, 283                                          recursion_level = self.recursion_level + 1, version = self.sigma_version, 284                                          Louvain_version = self.Louvain_version, verbose = self.verbose)285            if self.verbose:286                print("Sub-partition {1} found : {0}".format(self.format_partition(sub_Louvain.partition), i))287                288            # update new partition289            for individual in sub_Louvain.partition.keys():290                new_partition[individual] = sub_Louvain.partition[individual] + partition_number291                292                293            partition_number += len(set(sub_Louvain.partition.values()))294                295            if self.verbose:296                print("New partition now looks like: {0}".format(self.format_partition(new_partition)))297                298        self.partition = new_partition299                300        301    def format_partition(self, partition):302        if type(partition) == dict:303            return {str(self.name_of_node[id_code]) + "_" + str(id_code)[-4:] : partition[id_code] for id_code in partition.keys()}304        if type(partition) == list:305            return [[str(self.name_of_node[id_code]) + "_" + str(id_code)[-4:] for id_code in node_group] for node_group in partition]306        307            308        309            310                311    #def make_sub_graph(self, nodes = []):312    #    if not len(nodes):313    #        nodes = self.nodes314        315                316    def score_partition(self): 317        318        clusters=len(set(self.partition.values()))319        if self.verbose:320            print("Divided into {0} clusters".format(clusters))321        # analyse which cluster contains which members of each population322        cross_reference_dict=collections.defaultdict(lambda: collections.defaultdict(int))323        for node_id in self.partition.keys():324            cross_reference_dict["cluster {0}".format(self.partition[node_id])][self.name_of_node[node_id]]+=1325        326        cross_ref_df = pd.DataFrame.from_dict(cross_reference_dict,orient = 'index')327        if self.show_dfs:328            print(cross_ref_df)329        if len(self.store_results_dir):330            cross_ref_df.to_csv("{0}/{1}confusion_matrix.csv".format(self.store_results_dir,self.sigma_version))331            332            333            334        335        # make f-score based on true positive edges between ids in the same population336        TP=collections.defaultdict(int)337        tp=0338        FP=collections.defaultdict(int)339        fp=0340        FN=collections.defaultdict(int)341        fn=0342        for index in self.df_input_correlations.index:343            id1=self.df_input_correlations.loc[index]['object1']344            pop1=self.df_input_correlations.loc[index]['name1']345            id2=self.df_input_correlations.loc[index]['object2']346            pop2=self.df_input_correlations.loc[index]['name2']347            if pop1==pop2:348                if self.partition[id1]==self.partition[id2]:349                    TP[pop1]+=1 # true positive350                    tp+=1351                else:352                    FN[pop1]+=1 # false negative353                    fn+=1354            elif self.partition[id1]==self.partition[id2]:355                    FP[pop1]+=1 # false positive - not interested in true negatives356                    FP[pop2]+=1 # NB false positive edges double counted, once for each node (population object)357                    fp+=1358        359        FP_overall=sum([FP[name] for name in self.names])/2 # halved as double counted within populations360        TP_overall=sum([TP[name] for name in self.names])361        FN_overall=sum([FN[name] for name in self.names])362        assert FP_overall==fp363        assert TP_overall==tp364        assert FN_overall==fn365        366        recalls = {name :TP[name]/(TP[name]+FP[name]) if (TP[name] + FP[name]) else 0 for name in self.names}367        #print(recalls)368        precisions = {name : TP[name]/(TP[name]+FN[name]) if (TP[name] + FN[name]) else 0 for name in self.names}369        f_scores={name : 2*recalls[name]*precisions[name]/(recalls[name]+precisions[name]) if (recalls[name]+precisions[name]) else 0 for name in self.names}370        scores_df = pd.DataFrame([recalls,precisions,f_scores],index=['Recall','Precison','F-score'],columns=[name for name in self.names])371        R=TP_overall/(FP_overall+TP_overall)372        P=TP_overall/(TP_overall+FN_overall)373        f_score=2*R*P/(R+P)            374        scores_df['Overall'] = [R,P,f_score]375        if self.show_dfs:376            print(scores_df)377        if self.store_results_dir:378            scores_df.to_csv("{0}/{1}recall_precision_fscores.csv".format(self.store_results_dir,self.sigma_version))379    380        return {'{0}clusters'.format(self.sigma_version) :[clusters],'{0}recall'.format(self.sigma_version) :[R],'{0}precision'.format(self.sigma_version) : [P],'{0}f_score'.format(self.sigma_version) : [f_score]}381"""382def analyse_raw_results_for_scoring(td_object,reclustering=None,test_random_graph=False,383                                    repeats=1,pass_weights=True,verbose=False):384    raw_results=td_object.raw_results#[:400]385    if td_object.params.get("Use population means"):386        sigma_version='sigma_v2_'387    else:388        sigma_version='sigma_v1_'389    390    391    392    df_all = pd.DataFrame(raw_results,columns=['Z-score','object1','object2'])    393    # remove infinite/nan values394    df_all = df_all.replace(np.inf,np.nan)395    df_all=df_all.dropna(inplace=False)    396    # find p-values for each z-score397    df_all['p-value']=df_all['Z-score'].map(lambda x: stats.norm.cdf(x))398    399    # Extract the population names, object ids and time series for each time series object400    df_all['name1']=[d.name for d in df_all['object1']]401    df_all['name2']=[d.name for d in df_all['object2']]402    df_all['time series 1']=[d.t_series for d in df_all['object1']]403    df_all['time series 2']=[d.t_series for d in df_all['object2']]404    df_all['id1']=[id(d) for d in df_all['object1']]405    df_all['id2']=[id(d) for d in df_all['object2']]406    names=np.sort(list(set(df_all['name1']).union(set(df_all['name2']))))407    408    df_to_store=df_all.loc[:,['p-value','Z-score','name1','name2','id1','id2']]409            410    # create a group for each population411    df_matching=df_to_store.loc[df_to_store['name1'] == df_to_store['name2']]412    grouped_matching=df_matching.groupby(['name1'],as_index=False)413    df_new=pd.DataFrame()414    for group,frame in grouped_matching:415        #print(frame)416        df_new[group]=frame.mean()417    418    # store non-matching population results in a separate df419    df_non_matching=df_to_store.drop(labels=df_matching.index)   420    names_dict={name:i for i,name in enumerate(names)} # arbitrary order for names421    swap_dict=collections.defaultdict(int)422    for i in df_non_matching.index:423        name1=df_non_matching.loc[i]['name1']424        name2=df_non_matching.loc[i]['name2']425        if names_dict[name1]>names_dict[name2]:426            #swap names so that, for any pair, the order of names is always the same427            swap_dict[name1]+=1428            df_non_matching.at[i,'name1']=name2429            df_non_matching.at[i,'name2']=name1430    # group by both names431    group_non_matching=df_non_matching.groupby(['name1','name2'],as_index=False)432    if verbose:433        assert(len(group_non_matching)==len(names)*(len(names)-1)/2)434    df_non_matching_mean=pd.DataFrame()435    for group,frame in group_non_matching:436        df_non_matching_mean[group]=frame.mean()437    438    439    if verbose:440        print("Z score and corresponding p-value mean results within each population")441        print(df_new)442        print("Z score and corresponding p-value mean results across populations")443        print(df_non_matching_mean.head(2))444    445    z_across=df_non_matching['Z-score']446    z_within=df_matching['Z-score']447    if verbose:448        print("Mean non-matching stats is {0}".format([np.mean(z_across),np.std(z_across)]))449        print("Overall mean Z-score is {0}".format(np.mean(df_all['Z-score'])))450        print("Mean matching stats is {0}".format([np.mean(z_within),np.std(z_within)]))451    452    453    # store dataframes of results in csv files454    455    #df_non_matching.to_csv("{0}\{1}correlations_across_populations.csv".format(pc.TEMP_DIR,sigma_version))456    df_to_store.to_csv("{0}\{1}correlations.csv".format(pc.TEMP_DIR,sigma_version))457    458    if reclustering=='greedy Louvain':459        scores=[]460        j=0461        for i in range(repeats):462            if repeats%(int(repeats/100+1)):463                print("{0}%".format(j),end=',')464            j+=1465            print("\n")466            scores.append(make_partition_and_score(df_all,test_random_graph=test_random_graph,pass_weights=pass_weights,verbose=verbose))467        if repeats:468            print("Over {0} runs of graph and partition, scores are {1}".format(repeats,scores))469    470    return pd.DataFrame.from_dict({"matching": {'mean':np.mean(z_within),471                                                  "std": np.std(z_within)},472                                  "not matching" : {'mean': np.mean(z_across),473                                                       "std" : np.std(z_across)}474                                   },orient='index')...

new_scenario.py

Source:new_scenario.py

...73        parser_result = e74    scenario_base.store_parser_result(75        scenario_package_dirpath, parser_result,76        options.parser_result_tag)77    scenario_base.store_results_dir(78        scenario_package_dirpath, copied_dirpath)79    scenario_base.write_config(80        scenario_package_dirpath,81        status_version=harness.status_version,82        parser_result_tag=options.parser_result_tag,83        )84    scenario_base.install_unittest_module(85        scenario_package_dirpath, options.template_type)86    tmp_dirpath.clean()87if __name__ == '__main__':...

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